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Training LoRA Fine-Tuning - Optimize AI Models for IoT

Ref: VTX571
10 people max.
5500€ HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$170/day onsite · +$500 with certification exam
5 journées
distanciel

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Learning objectives

  • Master LoRA techniques for efficient fine-tuning in certified professional training
  • Develop skills in adapting AI models to IoT data in a business context
  • Design optimized fine-tuning pipelines for sensors
  • Implement LoRA on frameworks like Hugging Face for real IoT projects
  • Optimize model performance for edge deployment in a professional context
  • Deploy LoRA fine-tuned solutions integrating MQTT and LoRa

The Learni story

Founded by passionate learning and innovation experts, Learni's mission is to make professional training accessible to everyone, anywhere in the world. Our team operates in major hubs — London, New York, Boston — and internationally, to support talents and organizations in upskilling.

Don't let this gap widen

Why this program matters

  • Without this upskilling, your team accumulates a technological gap that translates directly into productivity loss.

  • Organizations that don't train their talents on key topics see their competitiveness drop.

  • Every quarter without training is a gap widening with competitors who invest.

  • The cost of inaction quickly exceeds that of well-targeted training.

Allan Busi
Allan Busi

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1LoRA Fine-Tuning Fundamentals: Principles and Setup (Hugging Face, PEFT)

Discover the basics of LoRA for cost-effective fine-tuning of LLMs adapted to IoT, install essential tools like Transformers and PEFT, complete your first exercises on simple sensor datasets, set up the remote environment for smooth practice, produce a basic fine-tuned model with precise evaluation metrics, apply to concrete IoT cases to anchor concepts from day one.

Module 2IoT Data Preparation: Sensors and MQTT/LoRa (Data Pipelines)

Dive into cleaning and augmenting data from IoT sensors via MQTT and LoRa, build pipelines with Pandas and Hugging Face Datasets, integrate real sensor signals for LoRA fine-tuning, test data quality with TensorBoard visualizations, generate training-ready datasets, simulate real-time IoT streams for immersive practical exercises.

Module 3LoRA Fine-Tuning Implementation: Scripts and Hyperparameters (PyTorch)

Move to practical LoRA implementation on models like BERT for IoT tasks, adjust hyperparameters like rank and alpha via grid search, run accelerated training on cloud GPU, monitor with Weights & Biases, optimize for low edge memory usage, produce saved checkpoints, evaluate gains compared to full fine-tuning on specific IoT benchmarks.

Module 4Advanced LoRA Optimization: QLoRA and IoT Deployment (ONNX, Docker)

Explore QLoRA for ultra-efficient quantized fine-tuning on constrained IoT devices, merge LoRA adapters into deployable models, convert to ONNX for fast inference, containerize with Docker for edge computing, integrate MQTT/LoRa for live streams, test performance on real sensor simulations, deliver an optimized prototype ready for enterprise production.

Module 5Practical Cases and LoRA Fine-Tuning Certification: Complete IoT Projects

Apply everything in capstone projects on sensor monitoring via LoRa and MQTT, fine-tune a model for IoT anomaly prediction, deploy in an end-to-end pipeline, present results with Streamlit dashboards, conduct peer reviews for cross-feedback, complete the certifying evaluation, leave with a concrete portfolio and professional skills to boost your IoT career.

Evaluation method

  • Interactive quizzes and MCQs after each module to validate learning outcomes
  • Graded practical case studies on LoRA IoT deployment
  • Final certifying project with report and live remote demo

Learning method

  • Active pedagogy with 70% hands-on practice on real IoT exercises
  • Individualized tutoring support in remote sessions
  • Video resources and source codes accessible post-training
  • Collaborative forum for participant exchanges

Methods, materials and delivery

The Training LoRA Fine-Tuning - Optimize AI Models for IoT program is delivered onsite or remote (blended-learning, e-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.

The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.

Equipment required

For the smooth delivery of the Training LoRA Fine-Tuning - Optimize AI Models for IoT program, the following equipment is required:

  • Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
  • Training environments installed on workstations or accessible online
  • Course materials, hands-on exercises and complementary resources
  • Post-training access to materials and educational resources

For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.

* contact us for remote delivery feasibility** ratio varies depending on the program

Skills assessment methods

Assessment of skills acquired during the Training LoRA Fine-Tuning - Optimize AI Models for IoT program is performed through:

  • During training: case studies, hands-on labs and professional scenarios
  • End of training: self-assessment questionnaire and skills evaluation by the trainer
  • After training: completion certificate detailing acquired skills

Program accessibility

Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.

Enrollment terms and lead times

Learni programs are available inter-company and intra-company, onsite or remote. Enrollments are possible up to 48 business hours before the program starts. Our programs are eligible for corporate funding paths. Contact us to discuss your training project and funding options.

Verified reviews

What our learners

4.9 · +100 verified reviews
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
Read all reviews
Our method

Training quality, guaranteed at every step

Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.

Step 1

Rigorous trainer selection

Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.

  • Triple validation: technical, pedagogical, sectoral.
  • Minimum rating 4.8/5 over the last 12 sessions.
Step 2

You meet the trainer beforehand

30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.

  • Live briefing on goals and team context.
  • Veto right — we swap the trainer for free if needed.
Step 3

Content tailored to your context

No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.

  • Hands-on cases drawn from your stack and projects.
  • Program co-written then validated by your team.
Step 4

Continuous quality follow-up

Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.

  • NPS, knowledge quizzes and skills self-assessment.
  • Satisfaction guarantee: fully satisfied or free rework.

A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.

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